GraphNews

What the heck does an ontology look like? How is it structured?

Discover how 2025 marked Neo4j's evolution from a graph database and analytics leader into the world’s premier graph intelligence platform.

Gaming giant teams with Deloitte to deploy a natural language query platform that uses LLM-powered agents and knowledge graphs to transform traditional user input-driven insight discovery.

A framework for transforming tabular (CSV, SQL) and hierarchical data (JSON, XML) into property graphs and ingesting them into graph databases (ArangoDB, Neo4j, TigerGraph). Features automatic PostgreSQL schema inference.

Implementing, Comparing, and Leveraging Neo4j, Zep, Cognee, Mem0, and Other Systems for Persistent, Explainable Intelligence

The single most undervalued fact of linear algebra: matrices are graphs, and graphs are matrices

In defining what an information taxonomy is, it's useful to understand what a taxonomy is not: classification, navigation, glossary, or ontology.

“We know that we need a KG. We just don’t know why — and how.”

In this post I want to bring some clarity on the different ways in which "semantics" contributes to AI. By "semantics" I mean here the understanding and modeling of data, which in turn leads to the use of supporting artefacts such as ontologies and knowledge grapIn this post I want to bring some cla

Enterprise Knowledge Graphs have become essential for unifying heterogeneous data and enforcing semantic governance. However, the construction of their underlying ontologies remains a...

Let me start with a definition that differs from popular ones (e.g.

Lesson 7: You Need a Methodology

If you lead data or technology for a large enterprise, you have probably lived through the same sequence of data migrations. Data warehouses gave way to

A Review of Standard Ontologies That Exist for Decades

When building graphs, it’s tempting to reuse the same relationship type names across different parts of the model. But in both Neo4j and…

📊 Context Graph Series 🧠 Beyond Knowledge Graphs and Hypergraphs : Context as a Control Plan (Graphs decide how you store. Context Graphs decide how you act.) There's a lot of discussion around graphs — knowledge graphs, hypergraphs, higher-order networks, world models. All important. But they're still answering the same question: 👉 How do we store complex information? Context Graphs answer a different question: 👉 How do we decide what can be acted on — here, now, and by whom? 🧱 The Storage Problem is Solved Knowledge Graphs gave us triples. Hypergraphs gave us hyperedges. Property Graphs gave us rich attributes. Storage keeps getting better. But enterprise AI systems still fail. Not because data is missing. Because decisions are reused without standing. 🏢 Real Enterprise Example: Atlas Systems Atlas has a knowledge graph. Entities. Relationships. Policies. It even has a hypergraph for multi-party vendor agreements. Storage was never the problem. An agent retrieves a valid SLA exception. ✔️ Correctly stored ✔️ Correctly retrieved ❌ Applied in wrong region ❌ After its validity window ❌ Without original authority $280K overrun. Compliance violation. The graph was correct. The decision was not. 🧠 What a Context Graph Actually Does A Context Graph sits above storage. It doesn't replace: 🔹 Knowledge Graphs 🔹 Hypergraphs 🔹 Property Graphs It works on top of all of them. Before any action, it asks: ✓ Who approved this? ✓ Under what authority? ✓ For which scope? ✓ During what time window? ✓ With what evidence? If standing matches → Execute If not → Block or Escalate That's governance at runtime — not after the fact. 🤖 Why This Matters for AI Agents AI agents don't invent most enterprise failures. They scale existing ones. Without a Context Graph, agents retrieve correctly, reason fluently, act confidently — and still fail audits. Because correctness is not permission. ⚠️ Wrong region? Not a data error. Past time window? Not a hallucination. No authority? Not a retrieval failure. It's a governance failure — no graph structure alone can prevent it. 🔍 Why This Conversation Is Accelerating Ashu Garg and Jaya Gupta helped name this shift — Context Graphs. Arvind Jain and Dharmesh Shah stress durable decision traces across systems. Kirk Marple calls it a clear signal of where enterprise AI is heading. The pattern is clear: storage is solved. Governance is not. 💡 Architect Takeaway The last generation of systems optimized for answers. The next generation must optimize for judgment. 🔹 Graphs decide how you store. 🔹 Context Graphs decide how you act. That's why this layer can't be bypassed. #ContextGraphs #EnterpriseAI #AgenticAI #AIArchitecture #KnowledgeGraphs #Hypergraphs #Governance Kurt Cagle Anthony Alcaraz Jessica Talisman Raphaël MANSUY | 10 comments on LinkedIn

In this annual segment, EK CEO Zach Wahl defines what he considers to be the top trends in Knowledge Management for 2026.

How I Turned 144 Pages of Regulation Into One Clickable Map.

Most of what we call a “knowledge graph” is still too close to the semantic layer mindset: great for measurement, weak on meaning.

The enterprise AI world has a new obsession: the Context Graph. Venture capital is flowing into it, and engineering teams are racing to build it.

A Practical Perspective Grounded in Labeled Property Graphs and Applied Knowledge Graphs

Table of Contents